Monochromatic edge geometry reconstruction through achromatic guidance

ABSTRACT

Many imaging scenarios involve an achromatic image (e.g., a panchromatic image or a near-infrared image) and one or more concurrently captured monochromatic images (e.g., RGB images captured through a Bayer filter array), and the compositing of these images through de-mosaicing and/or pan-sharpening to generate a high-resolution color image. However, in many such scenarios, the monochromatic images may exhibit distortion of edge geometry, resulting in artifacts and/or color distortions near visual edges of the composite image. However, such distortions may be absent from the achromatic image, and edge geometry may be represented as an intensity gradient among respective neighborhoods of achromatic pixels. Presented herein are techniques for reducing such distortions in monochromatic images through iterative adjustment of monochromatic pixel intensity to reflect the gradients of the neighborhoods of the corresponding achromatic pixels. Convergence of such adjustments produces composite images exhibiting accurately reconstructed edge geometry.

BACKGROUND

Within the field of imaging, many scenarios involve the generation of acomposite image using a set of monochromatic images, such as imagescaptured by respective image sensors respectively positioned behind red,green, and blue color filters of a Bayer filter array. In many suchscenarios, the pixels of respective input monochromatic images mayrepresent a mosaic, and each (achromatic) pixel of a composite image maybe generated through a “de-mosaicing” calculation based on the set ofcorresponding monochromatic pixels in approximately the same position ofthe monochromatic images. However, many such techniques may result inchromatic inaccuracies or artifacts, as the reconstruction of thecaptured color of each composite pixel from the mosaic of capturedmonochromatic images may not precisely and accurately reflect therelevant portion of the visible spectrum.

In addition, many such scenarios also involve an input achromatic imagecaptured through a different image sensor, such as a panchromatic imagecaptured by an unfiltered image sensor that captures a large range ofthe achromatic spectrum, or an infrared or near-infrared image capturedby an image sensor positioned behind an infrared-passing ornear-infrared-passing filter. As a first example, the inclusion ofachromatic pixels of the achromatic image with the correspondingmonochromatic pixels may facilitate accurate per-pixel colorreconstruction. For example, the luminance of a panchromatic pixel maybe compared with the composite luminance of the respective monochromaticpixels, and a proportional scaling may be applied to adjust theintensity of the monochromatic pixels to match the luminance of thepanchromatic pixel. As a second example, the monochromatic image may becaptured with a higher resolution than the monochromatic images. Apan-sharpening technique may be utilized to combine the color data fromthe lower-resolution monochromatic images and the higher resolution ofthe monochromatic image to produce a high-resolution, color compositeimage. As a third example, in some types of imaging, an achromatic imagemay capture particular types of information that are not fully capturedby the monochromatic images. For example, in aerial photography,infrared and near-infrared images may more accurately reflect edgedetail of trees and bodies of water than monochromatic images, and thecomposite image may result in more accurate edge detail for suchobjects. For these and other reasons, many cameras and image processingtechniques may utilize a combination of monochromatic and achromaticimages to generate composite images having various advantageousproperties.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

A particular feature of monochromatic imaging that may significantlyaffect the quality of a composite image generated through de-mosaicingand/or pan-sharpening is a subtle loss of geometry among the objectscaptured through monochromatic filters. For example, the particularshapes, orientation, and alignment of edges may be distorted by thelinear interpolation involved in composite image generation techniques.The resulting composite images may exhibit various forms of visualartifacts around the edges, such as Moiré patterns and colorinaccuracies. However, such inaccuracies may not be present inachromatic images. Additionally, such edge detail may be exhibited bygradients among neighboring pixels in the achromatic image. Adjustingthe pixels of the monochromatic images to according to gradients amongneighboring pixels of the achromatic image, may produce a compositeimage exhibiting reduced the edge geometry distortion from themonochromatic images.

Presented herein are techniques for generating a composite image from aset of monochromatic images and an achromatic image that reflect areconstruction of edge geometry, higher chromatic accuracy, and areduction of visual artifacts. In accordance with these techniques, thepixels of the monochromatic images may be subjected to an adjustment ofthe monochromatic pixels to reflect a gradient exhibited in thecorresponding pixel and neighboring pixels (e.g., a 3×3 grid) of theachromatic image. Additionally, such adjustment may be performediteratively, incrementally adjusting the monochromatic pixels to reflectthe gradient of the achromatic pixels, until a convergence of theadjustment is achieved. The present disclosure provides severalvariations in such techniques, as well as mathematical formulaeexpressing particular calculations that may enable a suitable iterativeadjustment in accordance with the techniques presented herein.

To the accomplishment of the foregoing and related ends, the followingdescription and annexed drawings set forth certain illustrative aspectsand implementations. These are indicative of but a few of the variousways in which one or more aspects may be employed. Other aspects,advantages, and novel features of the disclosure will become apparentfrom the following detailed description when considered in conjunctionwith the annexed drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary scenario featuring a compositeimage of a scene generated from a set of monochromatic images and anachromatic image.

FIG. 2 is an illustration of an exemplary scenario featuring acompositing of a composite image of a scene from a set of monochromaticimages and an achromatic image.

FIG. 3 is an illustration of an exemplary scenario featuring acompositing of a composite image of a scene from a set of monochromaticimages and an achromatic image in accordance with the techniquespresented herein.

FIG. 4 is an illustration of an exemplary method of generating acomposite image of a scene from a set of monochromatic images and anachromatic image in accordance with the techniques presented herein.

FIG. 5 is a component block diagram illustrating an exemplary cameraconfigured to generate a composite image of a scene from a set ofmonochromatic images and an achromatic image in accordance with thetechniques presented herein.

FIG. 6 is an illustration of an exemplary computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

FIG. 7 illustrates an exemplary computing environment wherein one ormore of the provisions set forth herein may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form in order to facilitatedescribing the claimed subject matter.

A. Introduction

Within the field of imaging, many scenarios involve the generation of acomposite image from a set of monochromatic images. For example, manycameras comprise a series of image sensors respectively positionedbehind a color filter that transmits light within a particular narrowmonochromatic range, and a compositing component that combines the colordata from the monochromatic images to generate a full-color compositeimage. In particular, for each pixel of the composite image, suchcameras may capture a “mosaic” of monochromatic pixels, such as a gridor cluster of nearby monochromatic pixels, and may “de-mosaic” thecluster to produce the full-color pixel of the composite image.

Moreover, many such scenarios may also supplement the compositing with adifferent type of image. As a first example, a camera may include apanchromatic image that captures light across the visible spectrum(e.g., an image sensor not positioned behind a chromatic filter), andthe compositing may factor the pixels of the panchromatic image into thecompositing. For example, the monochromatic pixels, factored together,may unevenly and/or incompletely reflect the full range of visible lightintensities. The camera may reduce inaccuracies in the intensity ofrespective composite pixels by comparing the sum of the intensities ofthe monochromatic pixels and the intensity of the correspondingpanchromatic pixel, and proportionally scaling the monochromatic pixelintensities to equalize this comparison. As a second example, in sometypes of imaging (such as aerial photography), particular image detailsmay not be fully captured by the particular monochromatic images, suchas the edges of trees and/or bodies of water. Therefore, the camera mayalso capture an infrared or near-infrared image that more accuratelycaptures such details, and the compositing of each pixel in thecomposite image may factor in the corresponding infrared ornear-infrared pixel in addition to the monochromatic pixels. As a thirdexample, some cameras may include monochromatic image sensors thatcapture comparatively low-resolution images, and a panchromatic imagesensor that captures a high-resolution image (e.g., in order to reducethe cost or data volume of the monochromatic image sensors), sincechrominance resolution may be less noticeable or significant for imagequality than luminance resolution. Accordingly, various compositingtechniques (“pan-sharpening” techniques) may involve factoring togetherthe respective and corresponding low-resolution pixels of themonochromatic images to calculate the color for more than one pixel ofthe panchromatic image to generate a high-resolution, full-colorcomposite image.

FIG. 1 presents an illustration of an exemplary scenario 100 involving acapturing of a scene 102 with a camera 104 that is configured togenerate a composite image 122 composited from a set of monochromaticimages 116 and an achromatic image 118. In this exemplary scenario 100,when oriented toward the scene 102, the camera 104 receives light fromthe scene 102 through a lens 106 and focuses such light on a set ofimage sensors 112, each comprising an array of photosensitive elements114 that respectively sample the intensity of light and generate anintensity value representing a pixel of the corresponding image.Moreover, respective image sensors 112 generate different types ofimages that are factored together to generate the composite image 122.For example, the camera 104 includes a Bayer color filter array 108,wherein respective image sensors 112 are positioned behind amonochromatic filter 110 (e.g., a red filter, a green filter, and a bluefilter), and produce a monochromatic image 116 based only on theintensities of the filtered range of the visible spectrum. Additionally,the camera 104 includes a fourth image sensor 112 that is not positionedbehind a monochromatic filter 110 (e.g., the camera 104 may include nofilter, or may include achromatic filters, such as light polarizers thatreduce glare), and that captures an achromatic image 118 and generates agrayscale composite image, where each pixel reflects the total intensityof the full spectrum of visible light. Having concurrently capturedthree chromatic images 116 and one achromatic image 118 of the scene102, the camera 104 perform an image compositing 120 that calculateseach pixel of the composite image 122 from the corresponding pixel ofeach monochromatic image 116 and the achromatic image 118. In thismanner, the camera 104 may produce a full-color composite image 122 withadditional image quality properties (e.g., brightness, contrast, coloraccuracy, and/or resolution) achieved by also factoring in theachromatic image 118.

B. Presented Techniques

As illustrated in the exemplary scenario 100 of FIG. 1, many cameras 104may be configured to utilize a variety of image sensors 112 to capturemonochromatic images 116 and one or more achromatic images 118, and maycomposite the images in order to enhance properties such as coloraccuracy, brightness, edge contrast, and/or resolution.

However, one property that many such techniques may not adequatelyaddress is a distortion of edge geometry of monochromatic images 116. Inimaging, because the contrast of visual edges may be reflected moresignificantly in luminance than chrominance, a composite image 122composited from monochromatic images 116 may not accurately reflect thegeometry of visual edges within the scene 102, and linear interpolationtechniques for reconstructing the image of the scene 102 may thereforeinaccurately reflect some details of such edges, including the shape,orientation, and/or alignment of such edges. The resulting compositeimage 122 may therefore exhibit some visual artifacts around the edges,such as a subtle mottling or speckling of color along visual edgeswithin the composite image 122.

FIG. 2 presents an illustration of an exemplary scenario 200 featuring acapturing of a scene 102 as a set of several monochromatic images 116(only one of which is depicted in FIG. 2) and an achromatic image 118,and the generation of a composite image 122 where each composite pixel208 is computed by factoring together the corresponding monochromaticpixel 204 of each monochromatic image 116 and the achromatic pixel 206of the achromatic image 118. As further illustrated in this exemplaryscenario 200, the scene 102 includes an edge 202 of an object, such as acurve of a ball. In the monochromatic image 116, respectivemonochromatic pixels 204 may not accurately reflect the geometry of theedge 202, because the intensity contrast between such values may have alower magnitude in this chrominance channel than in an achromaticluminance channel. These inaccuracies may be exacerbated in cameras 104featuring a lower-resolution monochromatic image sensor 112.Accordingly, the monochromatic pixels 204 comprising the monochromaticimage 116 may present a subtle inaccuracy or distortion along the edge202 within the scene 102. Similar inaccuracies may arise along the edges202 of the other monochromatic images 116 for similar reasons, such thatthe composite image 122 composited from the monochromatic images 116 mayreflect visual artifacts 210 along the edge 202, which may appear asspeckling, noise, a geometric distortion, and/or a mottling of coloralong the edge 202 in the composite image 122.

It may further be noted that an achromatic image 118 of the same scene102 that focuses on capturing luminance rather than chrominance may beless susceptible to such inaccuracies, as the edge geometry may presentmore significant luminance contrast. Additionally, if the achromaticimage 118 is captured with higher resolution, more data may be availablethat enables a more precise calculation of such edge geometry. However,many compositing techniques utilized by such cameras 104 may not accountfor such differential accuracy in the achromatic image 118 as comparedwith the monochromatic images 116. For example, rather than using themore accurate edge details in the achromatic image 118 to detect andreduce the edge inaccuracies in the corresponding pixels of themonochromatic images 116, such compositing techniques may simply factorthe corresponding pixels together (e.g., as an arithmetic average). Thatis, in such other techniques, the adjustment of the monochromatic pixels204 may only relate to the corresponding achromatic pixel 206, and mayobscure, rather than conform with, the edges and edge details that areencoded in the achromatic image 118. Accordingly the inaccuracies in themonochromatic images 116 may persist and appear in the composite image122.

Presented herein are techniques for generating a composite image 122composited from one or more monochromatic images 116 and an achromaticimage 118 (e.g., a panchromatic image, or an infrared or near-infraredimage) that include a reconstruction of edge geometry. In accordancewith these techniques, and as further depicted in the exemplary scenario200 of FIG. 2, it may be appreciated that in the achromatic image 118,an edge 202 may be represented as a gradient 214 of intensity within aneighborhood 212 of the achromatic pixel 206. Accordingly, for eachcomposite pixel 208 in the composite image 122, the correspondingmonochromatic pixels 204 of the monochromatic images 116 may be comparednot only with the intensity of the corresponding achromatic pixel 206 inthe achromatic image 118, but with a gradient 214 of the neighborhood212 of the achromatic pixel 206 in the achromatic image 118. Moreover,for respective pixels 208 of the composite image 122, an iterativeprocess may be performed to adjust the intensities of the respectivecorresponding monochromatic pixels 204 of the monochromatic images 116to reflect the gradient 214 exhibited in the neighborhood 212 of thecorresponding achromatic 206 pixel of the achromatic image 118. Thisiterative processing may be performed a desired number of times (e.g., afixed number of iterations, or until an adjustment convergence isdetected), and the adjusted monochromatic pixels 204 may be factoredtogether to produce the composite pixel 208 of the composite image 122.

FIG. 3 presents an illustration of an exemplary scenario featuring anexemplary iterative process for adjusting a set of monochromatic pixels204 to reflect a gradient 214 that is apparent in a neighborhood 212 ofthe corresponding achromatic pixel 206 of an achromatic image 118. Inthis exemplary scenario, at a first time 300 (e.g., before iterating), acaptured achromatic image 118 may reflect a gradient in intensityrepresenting a visual edge 202 within the scene 102. However, thisgradient 214 may be less distinct in respective monochromatic images,including a red image, a green image, and a blue image, due to thecomparatively lower magnitude of the contrast in the chrominance channelthan the luminance channel. For example, the gradient 214 may bereflected as a transition between a light color and a brown color thatis represented as a combination of red and green monochromatic pixels204, which may be more apparent in the achromatic image 118 than in theindividual monochromatic images 116 or the combination thereof. As aresult, while the gradient 216 may appear sharp and distinct in theachromatic image 118, visual artifacts may appear along the edge 202 inthe red and green monochromatic images 116 (and may not appear in theblue monochromatic image 116 that does not contribute to the gradient214). In order to reduce such visual artifacts, an iterative adjustmentmay be applied by incrementally migrating the intensities of themonochromatic pixels 204 toward the gradient 214 in the neighborhood 212of the achromatic image 118. For example, at a second time point 302(e.g., following one or more iterations), the monochromatic pixels 204in each of the red and green image may exhibit a more distinct gradientthat matches the gradient 214 of the achromatic image 118; and at athird time point 304 (e.g., at an adjustment convergence of theiterative process), the red monochromatic image 116 and the greenmonochromatic image 116 may together clearly and consistently depict thegradient 214 reflected in the neighborhood 212 of the achromatic image118. The resulting adjusted monochromatic images 116 may then becomposited to generate a composite image 122 reflecting a reconstructionof the edge geometry captured in the achromatic image 118 in accordancewith the techniques presented herein.

C. Exemplary Embodiments

FIG. 4 presents a first exemplary embodiment of the techniques presentedherein, illustrated as an exemplary method 400 of generating a compositeimage 122 of a scene 102 from an achromatic image 118 of the scene 102and at least two monochromatic images 116 of the scene 102. Theexemplary method 400 may be implemented, e.g., as a set of instructionsstored in a memory component of the device, such as a memory circuit, aplatter of a hard disk drive, a solid-state storage device, or amagnetic or optical disc, and organized such that, when executed by thedevice (e.g., on a processor of the device), cause the device to operateaccording to the techniques presented herein. The exemplary method 400begins at 402 and involves executing 404 the instructions on a processorof the device. Specifically, these instructions may be configured to,for respective composite pixels 208 of the composite image 122corresponding to an achromatic pixel 206 of the achromatic image 118 andat least one monochromatic pixel 204 of respective monochromatic images116, adjust 406 the monochromatic pixels 204 according to a gradient 214between the achromatic pixel 206 and neighboring achromatic pixels 206to produce a set of adjusted monochromatic pixels 204. The instructionsare also configured to compute 408 the composite pixels 208 of thecomposite image 122 from the adjusted monochromatic pixels 204. Havingachieved the generation of the composite image 122 by adjusting themonochromatic pixels 204 according to the gradients 214 amongneighborhoods 212 of achromatic pixels 206, the exemplary method 400achieves the techniques presented herein, and so ends at 410.

FIG. 5 presents a second exemplary embodiment of the techniquespresented herein, illustrated as an exemplary camera 502 configured togenerate a composite image 122 of a scene 102. The camera 502 comprisesat least one achromatic image sensor 504 that is configured to capturean achromatic image 118 of the scene 102, and at least two monochromaticimage sensors 506 respectively configured to capture, concurrently withthe achromatic image 118, a monochromatic image 116 of the scene 102.The exemplary camera 502 also includes an image generating component 508that is configured to generate a composite image 122 of the scene 102using the achromatic image 118 and the at least two monochromatic images116. The image generating component 508 may be implemented, e.g., as aset of instructions stored in a memory of the camera 502 and executableon a processor of the camera 502; a digital logic circuit; afield-programmable gate array (FPGA) configured to express the logic ofthe techniques provided herein; or any similar architecture or device.More specifically, the image generating component 508 achieves thisresult by, for respective composite pixels 208 of the composite image122 corresponding to an achromatic pixel 206 of the achromatic image 118and at least one monochromatic pixel 204 of respective at least twomonochromatic images 116, adjusting the monochromatic pixels 204according to a gradient 214 between the achromatic pixel 206 andneighboring achromatic pixels 206 of the achromatic image 118 to producea set of adjusted monochromatic pixels 204; and computing the compositepixels 208 of the composite image 12 from the adjusted monochromaticpixels 204. In this manner, the exemplary camera 502 may generate thecomposite image 122 from the achromatic image 118 and the at least twomonochromatic images 116 in accordance with the techniques presentedherein.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to apply the techniquespresented herein. Such computer-readable media may include, e.g.,computer-readable storage media involving a tangible device, such as amemory semiconductor (e.g., a semiconductor utilizing static randomaccess memory (SRAM), dynamic random access memory (DRAM), and/orsynchronous dynamic random access memory (SDRAM) technologies), aplatter of a hard disk drive, a flash memory device, or a magnetic oroptical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set ofcomputer-readable instructions that, when executed by a processor of adevice, cause the device to implement the techniques presented herein.Such computer-readable media may also include (as a class oftechnologies that are distinct from computer-readable storage media)various types of communications media, such as a signal that may bepropagated through various physical phenomena (e.g., an electromagneticsignal, a sound wave signal, or an optical signal) and in various wiredscenarios (e.g., via an Ethernet or fiber optic cable) and/or wirelessscenarios (e.g., a wireless local area network (WLAN) such as WiFi, apersonal area network (PAN) such as Bluetooth, or a cellular or radionetwork), and which encodes a set of computer-readable instructionsthat, when executed by a processor of a device, cause the device toimplement the techniques presented herein.

An exemplary computer-readable medium that may be devised in these waysis illustrated in FIG. 6, wherein the implementation 600 comprises acomputer-readable medium 602 (e.g., a CD-R, DVD-R, or a platter of ahard disk drive), on which is encoded computer-readable data 604. Thiscomputer-readable data 604 in turn comprises a set of computerinstructions 606 configured to operate according to the principles setforth herein. In a first such embodiment, the processor-executableinstructions 606 may be configured to perform a method 608 of generatinga composite image 122 from a monochromatic image 118 and at least onemonochromatic image 116, such as the exemplary method 400 of FIG. 4. Ina second such embodiment, the processor-executable instructions 606 maybe configured to implement systems for generating a composite image 122from a monochromatic image 118 and at least one monochromatic image 116,such as the image generating component 508 in the exemplary camera 502illustrated in the exemplary scenario 500 of FIG. 5. Some embodiments ofthis computer-readable medium may comprise a computer-readable storagemedium (e.g., a hard disk drive, an optical disc, or a flash memorydevice) that is configured to store processor-executable instructionsconfigured in this manner. Many such computer-readable media may bedevised by those of ordinary skill in the art that are configured tooperate in accordance with the techniques presented herein.

D. Variations

The techniques discussed herein may be devised with variations in manyaspects, and some variations may present additional advantages and/orreduce disadvantages with respect to other variations of these and othertechniques. Moreover, some variations may be implemented in combination,and some combinations may feature additional advantages and/or reduceddisadvantages through synergistic cooperation. The variations may beincorporated in various embodiments (e.g., the exemplary method 400 ofFIG. 4, and/or the exemplary camera 502 of FIG. 5) to confer individualand/or synergistic advantages upon such embodiments.

D1. Scenarios

A first aspect that may vary among embodiments of these techniquesrelates to the scenarios wherein such techniques may be utilized.

As a first variation of this first aspect, the techniques presentedherein may be utilized with many types of image processing devices, suchas cameras, servers, server farms, workstations, laptops, tablets,mobile phones, game consoles, and network appliances. Such imageprocessing devices may also provide a variety of computing components,such as wired or wireless communications devices; human input devices,such as keyboards, mice, touchpads, touch-sensitive displays,microphones, and gesture-based input components; automated inputdevices, such as still or motion cameras, global positioning service(GPS) devices, and other sensors; output devices such as displays andspeakers; and communication devices, such as wired and/or wirelessnetwork components. Additionally, the image processing device mayperform the compositing in realtime or near-realtime (e.g., promptlyupon capturing the achromatic image 118 and the at least onemonochromatic image 116), or may perform the compositing at a later time(e.g., for stored images that were previously captured).

As a second variation of this first aspect, these techniques may beutilized in many types of imaging scenarios involving at least oneachromatic image 118 and at least one monochromatic image 116, such asaerial imaging; low-light imaging; underwater imaging; long-exposureimaging, such as astronomical photography; short-exposure imaging;time-lapse imaging; three-dimensional and/or stereoscopic imaging;motion photography, such as video and motion capture imaging; imagerecognition imaging, such as medical and/or security imaging; machinevision; microscopic imaging, such as electron microscopy; opticalcharacter recognition; and gesture recognition.

As a third variation of this first aspect, the monochromatic images 116may respectively involve data from many monochromatic ranges of thevisible or invisible electromagnetic spectrum. Additionally, themonochromatic image sensors 506 may capture the monochromatic images 116in many ways, such as an array of photosensitive elements 114 that aresensitive only to narrow ranges of the electromagnetic spectrum, andfilters positioned between the scene 102 and the image sensor 112.

As a fourth variation of this first aspect, the achromatic image 118 mayinvolve achromatic image types, e.g., data from many ranges of thevisible or invisible electromagnetic spectrum. For example, theachromatic image 118 may comprise a panchromatic image representing apanchromatic image type, representing a full-spectrum luminanceintensity. Alternatively, the achromatic image 118 may comprise specificvisible and/or invisible ranges of the electromagnetic spectrum, such asan infrared image type or a near-infrared image type. That is, theachromatic image 118 may encode or include a chrominance component, butthe techniques presented herein may not use the chrominance component inthe adjustment of the monochromatic pixels 204. Many such spectralranges may be captured to generate a suitable achromatic image 118 thatexhibits gradients 214 usable to reconstruct edge geometry in the atleast one monochromatic image 116. These and other scenarios may besuitable for the application of the techniques presented herein.

D2. Exemplary Adjustment Techniques

A second aspect that may vary among embodiments of these techniquesinvolves the manner of adjusting the monochromatic pixels 204 in view ofthe gradients 214 exhibited by a neighborhood 212 of the correspondingachromatic pixel 206.

As a first variation of this second aspect, the adjusting may involvevarious neighborhoods 212 of the achromatic pixel 206. As a first suchexample, the neighborhood 212 may comprise the directly laterallyadjacent set of achromatic pixels 206 (e.g., the two achromatic pixels206 to the left and right of the selected achromatic pixel 206, and/orthe two achromatic pixels 206 above and below the selected achromaticpixel 206). As a second such example, the neighborhood 212 may comprisethe laterally and diagonally adjacent achromatic pixels 206 tot theselected achromatic pixel 206 (e.g., a 3×3 grid). As other suchexamples, the neighborhood 212 may comprise a larger grid (e.g., a 5×5grid) and/or a differently shaped grid (e.g., a 3×5 grid). As a fourthsuch example, the gradient 214 may attribute achromatic pixel weights tothe neighboring achromatic pixels 206, e.g., based on the magnitude ofthe gradient 214 (e.g., the difference in luminance intensity betweenthe selected achromatic pixel 206 and the neighboring achromatic pixels206 s) and/or the distance from the selected achromatic pixel 206 to theneighboring achromatic pixel 206. As a fifth such example, gradients 214may be actively identified in the achromatic image 118 (e.g., using anedge detection technique), and such edges may be used to adjust themonochromatic pixels 204 of respective monochromatic images 116.

As a second variation of this second aspect, the dimensions of themonochromatic images 116 and/or achromatic image 118 may vary, bothgenerally and relative to one another. The adjustment may account forsuch variable dimensions in various ways. As a first such example, amonochromatic image 116 may have a lower resolution than the compositeimage 122 to be generated, and respective monochromatic pixels 204 maybe factored into more than one composite pixel 208 (e.g., distributingthe chrominance of a monochromatic pixel 204 fractionally over at leasttwo composite pixels 208). Conversely, the monochromatic image 116 mayhave a higher resolution than the composite image 122 to be generated,and at least two monochromatic pixels 204 may be factored into onecomposite pixel 208 (e.g., averaging the chrominance of suchmonochromatic pixels 204, optionally with a fractional weight based onthe degree of overlap).

As a second example of this second variation, a first monochromaticimage 116 may comprise a different number of monochromatic pixels 204corresponding to a composite pixel 208 than the number of correspondingmonochromatic pixels 204 in a second monochromatic image 116. Forexample, in an exemplary Bayer color filter array, respective compositepixels 208 are computed from a 2×2 block of monochromatic pixels 204comprising one red monochromatic pixel 204, one blue monochromatic pixel204, and two green monochromatic pixels 204. In order to factorrespective monochromatic pixels 204 proportionately into the compositepixel 208, where at least one selected monochromatic image 116 has atleast two monochromatic pixels 204 corresponding to respective compositepixels 208 of the composite image 122 to be generated, the adjustment ofthe monochromatic pixels 204 may involve adjusting the at least twomonochromatic pixels 204 of the selected monochromatic image 116according to not only the gradient 214 between the achromatic pixel 206and neighboring achromatic pixels 206 of the neighborhood 212, but alsoaccording to a monochromatic pixel weight that is inversely proportionalto a count of the monochromatic pixels 204 of the selected monochromaticimage 116 corresponding to the composite pixel 208. For example, inmonochromatic images 116 captured according to the Bayer color filterwith twice as many green pixels, respective green pixels may be factoredinto the composite pixel 208 with a 50% monochromatic pixel weight ascompared with the red monochromatic pixel 204 and the corresponding bluemonochromatic pixel 204.

As a third variation of this second aspect, the adjustment of themonochromatic pixels 204 may involve a smoothing weight constant thataffects the rate of adjustment of the monochromatic pixels in view ofthe gradient 214 of the neighborhood 212 of the corresponding achromaticpixel 206. For example, a first smoothing weight may result a moreaggressive smoothing that tightly adjusts the monochromatic images 116toward the gradient 214 of the achromatic image 118, while a secondsmoothing weight may result in a more gradual and/or conservativesmoothing. In addition, it may be advantageous to select the smoothingweight constant in view of various properties of the achromatic image118 and/or the monochromatic images 116, such as a noise level presentin one or several such images. Moreover, in some embodiments, thesmoothing weight constant may be selected by a user of a camera 502 oran image processor, while other embodiments may automatically select thesmoothing weight constant (e.g., in view of a detected noise level ofone or more images).

As a fourth variation of this second aspect, the adjusting may beapplied once, or for a specific, selected number of iterations.Alternatively, the adjusting may be iteratively applied until anadjustment convergence is detected, e.g., where a measurement of theadjustment of the monochromatic pixels 204 satisfies a convergencethreshold. Such convergence may be detected, e.g. based on the magnitudeof the adjustments of the monochromatic pixels 204 in respectiveiterations, and/or based on a degree of conformity of the monochromaticimages 116 and the gradients 214 apparent in the achromatic image 118.

As a fifth variation of this second aspect, the adjusting may beperformed according to various calculations. The following mathematicalformulae provide some examples of suitable embodiments implementingvarious aspects and variations of the techniques presented herein.

As a first example of this fifth variation, during respective incrementsof the adjusting, the monochromatic pixels 204 of respectivemonochromatic images 116 may be adjusted according to a firstmathematical formula comprising:

$u_{p}^{t + 1} = \frac{{{dw}_{p}*u_{p}} + {\sum\limits_{n \in N_{p}}\;{{{sw}\left( {p,n} \right)}*u_{n}^{t}}}}{{dw}_{p} + {\sum\limits_{n \in N_{p}}\;{{sw}\left( {p,n} \right)}}}$

wherein:

-   -   t represents an iteration of the iterative adjusting;    -   p represents respective composite pixels 208;    -   u_(p) represents the adjusted monochromatic pixels 204        corresponding to composite pixel p;    -   dw_(p) represents a monochromatic pixel weight;    -   N_(p) represents a neighborhood 212 of neighboring achromatic        pixels of the achromatic pixel 206;    -   n represents respective neighboring achromatic pixels 206 of the        neighborhood 212;    -   sw(p,n) represents a gradient between the achromatic pixel 206        corresponding to the composite pixel p and respective        neighboring achromatic pixels n; and    -   u_(p) ^(t) represents the adjusted monochromatic pixels 204        corresponding to the composite pixel p adjusted during iteration        t, where a first iteration is computed according to a second        mathematical formula comprising:        u _(p) ⁰=(i _(p) −m)

wherein:

-   -   m represents the achromatic pixel 206 corresponding to the        composite pixel p, and    -   i_(p) represents respective monochromatic pixels 204        corresponding to the composite pixel p.

As a second example of this fifth variation, the adjustment of themonochromatic pixels 204 of respective monochromatic images 116 may beadjusted according to a third mathematical formula comprising:

${dw}_{p} = \begin{Bmatrix}\begin{Bmatrix}w_{r} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{red}} \\0 & {else}\end{Bmatrix} \\\begin{Bmatrix}w_{g} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{green}} \\0 & {else}\end{Bmatrix} \\\begin{Bmatrix}w_{b} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{blue}} \\0 & {else}\end{Bmatrix}\end{Bmatrix}$

wherein:

-   -   p represents respective composite pixels 208;    -   dw_(p) represents the monochromatic pixel weight of respective        monochromatic pixels 204;    -   w_(r) represents a red monochromatic pixel weight of respective        red monochromatic pixels 204 that is inversely proportional to a        count of the red monochromatic pixels 204 corresponding to the        composite pixel 208;    -   w_(g) represents a green monochromatic pixel weight of        respective green monochromatic pixels 204 that is inversely        proportional to a count of the green monochromatic pixels 204        corresponding to the composite pixel 208; and    -   w_(b) represents a blue monochromatic pixel weight of respective        blue monochromatic pixels 204 that is inversely proportional to        a count of the blue monochromatic pixels 204 corresponding to        the composite pixel 208.

As a third example of this fifth variation, the adjustment of themonochromatic pixels 204 may involve calculating the gradient 214 of theneighborhood 212 of the corresponding achromatic pixel 206 and thesmoothing weight constant according to a fourth mathematical formulacomprising:

${{sw}\left( {p,n} \right)} = \frac{eps}{{eps} + \left( {n - m} \right)^{2}}$

wherein:

-   -   p represents respective composite pixels 208;    -   m represents the achromatic pixel 206 corresponding to the        composite pixel p;    -   n represents respective neighboring achromatic pixels 206 of the        achromatic pixel m; and    -   eps represents the smoothing weight constant.

As a fourth example of this fifth variation, after completing theadjusting, the composite pixels 208 of the composite image 122 may becomputed from the adjusted monochromatic pixels 204 according to a fifthmathematical formula comprising:c _(p)=(u _(p) ^(t) ^(c) +m)

wherein t_(c) represents a final iteration resulting in an adjustmentconvergence of the monochromatic pixels 204 (e.g., adding back in theintensity of the achromatic pixel 206 that was removed during theinitial iteration). These and other variations and mathematical formulaemay be applied to achieve the adjusting of the monochromatic pixels 204in view of the gradients 214 in the neighborhood 212 of thecorresponding achromatic pixel 206 in view of the techniques presentedherein.

D3. Composite Image Generation

A third aspect that may vary among embodiments of the techniquespresented herein involves the generation of the composite image 122 fromthe adjusted monochromatic pixels 204.

As a first such variation, in addition to more accurate edge geometryand a reduction of visual artifacts, the techniques presented herein mayresult in various effects for the composite image 122. As a first suchexample, the factoring of the monochromatic pixels 204 during thesetechniques may result in a de-mosaicing of the monochromatic pixels 204(e.g., factoring a 2×2 cluster of red, green, and blue pixels generatedby a Bayer filter array into a full-color composite pixel 208). As asecond such example, the adjustment of the monochromatic pixels 204factored together with the achromatic pixels 206 of a potentiallyhigher-resolution achromatic image 118 may result in a pan-sharpening ofthe composite image 122.

As a second such variation, various other image processing techniquesmay be performed before, during, or after the adjusting of themonochromatic pixels 204 and/or the generation of the composite image122. As one such example, an embodiment may also apply an edgepreservation calculation to the composite image 122, such as ananisotropic diffusion edge preservation calculation; a bilateralfiltering edge preservation calculation; and/or as vectorial totalvariation edge preservation calculation. These and other additionalcalculations and results may result in the composite image 122 generatedaccording to the techniques presented herein.

E. Computing Environment

FIG. 7 and the following discussion provide a brief, general descriptionof a suitable computing environment to implement embodiments of one ormore of the provisions set forth herein. The operating environment ofFIG. 7 is only one example of a suitable operating environment and isnot intended to suggest any limitation as to the scope of use orfunctionality of the operating environment. Example computing devicesinclude, but are not limited to, personal computers, server computers,hand-held or laptop devices, mobile devices (such as mobile phones,Personal Digital Assistants (PDAs), media players, and the like),multiprocessor systems, consumer electronics, mini computers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Although not required, embodiments are described in the general contextof “computer readable instructions” being executed by one or morecomputing devices. Computer readable instructions may be distributed viacomputer readable media (discussed below). Computer readableinstructions may be implemented as program modules, such as functions,objects, Application Programming Interfaces (APIs), data structures, andthe like, that perform particular tasks or implement particular abstractdata types. Typically, the functionality of the computer readableinstructions may be combined or distributed as desired in variousenvironments.

FIG. 7 illustrates an example of a system 700 comprising a computingdevice 702 configured to implement one or more embodiments providedherein. In one configuration, computing device 702 includes at least oneprocessing unit 706 and memory 708. Depending on the exact configurationand type of computing device, memory 708 may be volatile (such as RAM,for example), non-volatile (such as ROM, flash memory, etc., forexample) or some combination of the two. This configuration isillustrated in FIG. 7 by dashed line 704.

In other embodiments, device 702 may include additional features and/orfunctionality. For example, device 702 may also include additionalstorage (e.g., removable and/or non-removable) including, but notlimited to, magnetic storage, optical storage, and the like. Suchadditional storage is illustrated in FIG. 7 by storage 710. In oneembodiment, computer readable instructions to implement one or moreembodiments provided herein may be in storage 710. Storage 710 may alsostore other computer readable instructions to implement an operatingsystem, an application program, and the like. Computer readableinstructions may be loaded in memory 708 for execution by processingunit 706, for example.

The term “computer readable media” as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions or other data. Memory 708 and storage 710 are examples ofcomputer storage media. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, Digital Versatile Disks (DVDs) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by device 702. Anysuch computer storage media may be part of device 702.

Device 702 may also include communication connection(s) 716 that allowsdevice 702 to communicate with other devices. Communicationconnection(s) 716 may include, but is not limited to, a modem, a NetworkInterface Card (NIC), an integrated network interface, a radio frequencytransmitter/receiver, an infrared port, a USB connection, or otherinterfaces for connecting computing device 702 to other computingdevices. Communication connection(s) 716 may include a wired connectionor a wireless connection. Communication connection(s) 716 may transmitand/or receive communication media.

The term “computer readable media” may include communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” may include a signal that has one or moreof its characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 702 may include input device(s) 714 such as keyboard, mouse, pen,voice input device, touch input device, infrared cameras, video inputdevices, and/or any other input device. Output device(s) 712 such as oneor more displays, speakers, printers, and/or any other output device mayalso be included in device 702. Input device(s) 714 and output device(s)712 may be connected to device 702 via a wired connection, wirelessconnection, or any combination thereof. In one embodiment, an inputdevice or an output device from another computing device may be used asinput device(s) 714 or output device(s) 712 for computing device 702.

Components of computing device 702 may be connected by variousinterconnects, such as a bus. Such interconnects may include aPeripheral Component Interconnect (PCI), such as PCI Express, aUniversal Serial Bus (USB), Firewire (IEEE 1394), an optical busstructure, and the like. In another embodiment, components of computingdevice 702 may be interconnected by a network. For example, memory 708may be comprised of multiple physical memory units located in differentphysical locations interconnected by a network.

Those skilled in the art will realize that storage devices utilized tostore computer readable instructions may be distributed across anetwork. For example, a computing device 720 accessible via network 718may store computer readable instructions to implement one or moreembodiments provided herein. Computing device 702 may access computingdevice 720 and download a part or all of the computer readableinstructions for execution. Alternatively, computing device 702 maydownload pieces of the computer readable instructions, as needed, orsome instructions may be executed at computing device 702 and some atcomputing device 720.

F. Usage of Terms

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used in this application, the terms “component,” “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

Various operations of embodiments are provided herein. In oneembodiment, one or more of the operations described may constitutecomputer readable instructions stored on one or more computer readablemedia, which if executed by a computing device, will cause the computingdevice to perform the operations described. The order in which some orall of the operations are described should not be construed as to implythat these operations are necessarily order dependent. Alternativeordering will be appreciated by one skilled in the art having thebenefit of this description. Further, it will be understood that not alloperations are necessarily present in each embodiment provided herein.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as advantageousover other aspects or designs. Rather, use of the word exemplary isintended to present concepts in a concrete fashion. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims may generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated exemplary implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A method of generating a composite image of ascene from an achromatic image of the scene and at least twomonochromatic images of the scene on a device having a processor, themethod comprising: executing on the processor instructions configured tofor at least one composite pixel of the composite image corresponding toan achromatic pixel of the achromatic image and at least onemonochromatic pixel of respective monochromatic images, iterativelyadjust the at least one monochromatic pixel proportional to a differencebetween the achromatic pixel and neighboring achromatic pixels toproduce at least one adjusted monochromatic pixel in a set of adjustedmonochromatic pixels; and compute the composite pixels of the compositeimage from the adjusted monochromatic pixels.
 2. The method of claim 1,wherein the instructions are further configured to capture therespective monochromatic images of the scene by applying a monochromaticfilter to an image sensor.
 3. The method of claim 1, wherein theachromatic image having an achromatic image type selected from anachromatic image type set comprises: a panchromatic image type; and anear-infrared image type.
 4. The method of claim 1, wherein theneighboring achromatic pixels of a selected achromatic pixel comprisesthe achromatic pixels of the achromatic image that are laterally anddiagonally adjacent to the selected achromatic pixel.
 5. The method ofclaim 1, wherein adjusting the monochromatic pixels comprises: removingthe achromatic pixel from respective monochromatic pixels; and afteriterative adjustment, adding the achromatic pixel to respectivemonochromatic pixels.
 6. The method of claim 5, wherein the iterativeadjusting comprises: applying a selected number of iterations ofadjusting the respective monochromatic pixels.
 7. The method of claim 5,wherein the iterative adjusting comprises: iteratively adjusting therespective monochromatic pixels until reaching an adjustment convergenceof the monochromatic pixels satisfying a convergence threshold.
 8. Themethod of claim 5, wherein the instructions are further configured to:iteratively adjust the monochromatic pixels according to a firstmathematical formula comprising:$u_{p}^{t + 1} = \frac{{{dw}_{p}*u_{p}} + {\sum\limits_{n \in N_{p}}\;{{{sw}\left( {p,n} \right)}*u_{n}^{t}}}}{{dw}_{p} + {\sum\limits_{n \in N_{p}}\;{{sw}\left( {p,n} \right)}}}$wherein: t represents an iteration of the iterative adjusting; prepresents respective composite pixels; u_(p) represents the adjustedmonochromatic pixels corresponding to composite pixel p; dw_(p)represents a monochromatic pixel weight; N_(p) represents a neighborhoodof neighboring achromatic pixels of the achromatic pixel; n representsrespective neighboring achromatic pixels of the neighborhood; sw(p,n)represents a gradient between the achromatic pixel corresponding to thecomposite pixel p and respective neighboring achromatic pixels n; andu_(p) ^(t) represents the adjusted monochromatic pixels corresponding tothe composite pixel p adjusted during iteration t, where a firstiteration is computed according to a second mathematical formulacomprising:u _(p) ^(o)=(i _(p)−m) wherein: m represents the achromatic pixelcorresponding to the composite pixel p, and i_(p) represents respectivemonochromatic pixels corresponding to the composite pixel p and computethe composite pixels of the composite image from the adjustedmonochromatic pixels according to a third mathematical formulacomprising:c _(p)=(u _(p) ^(t) ^(c) +m) wherein t_(c) represents an iterationresulting in an adjustment convergence of the monochromatic pixels. 9.The method of claim 1, wherein, at least one selected monochromaticimage has at least two monochromatic pixels corresponding to respectivecomposite pixels; and the adjusting comprising: adjusting the at leasttwo monochromatic pixels of the selected monochromatic image accordingto the gradient between the achromatic pixel and the neighboringachromatic pixels.
 10. The method of claim 9, wherein the adjustingfurther comprises: adjusting the at least two monochromatic pixels ofthe selected monochromatic image according to a monochromatic pixelweight that is inversely proportional to a count of the monochromaticpixels of the selected monochromatic image.
 11. The method of claim 10,wherein the instructions are further configured to adjust themonochromatic pixels of the selected monochromatic image according tothe monochromatic pixel weight according to a mathematical formulacomprising: ${dw}_{p} = \begin{Bmatrix}\begin{Bmatrix}w_{r} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{red}} \\0 & {else}\end{Bmatrix} \\\begin{Bmatrix}w_{g} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{green}} \\0 & {else}\end{Bmatrix} \\\begin{Bmatrix}w_{b} & {{if}\mspace{14mu}{pixel}\mspace{14mu}{is}\mspace{14mu}{blue}} \\0 & {else}\end{Bmatrix}\end{Bmatrix}$ wherein: p represents respective composite pixels; dw_(p)represents the monochromatic pixel weight of the monochromatic pixels ofrespective monochromatic images; w_(r) represents a red monochromaticpixel weight of respective red monochromatic pixels that is inverselyproportional to a count of the red monochromatic pixels corresponding tothe composite pixel; w_(g) represents a green monochromatic pixel weightof respective green monochromatic pixels that is inversely proportionalto a count of the green monochromatic pixels corresponding to thecomposite pixel; and w_(b) a blue monochromatic pixel weight ofrespective blue monochromatic pixels that is inversely proportional to acount of the blue monochromatic pixels corresponding to the compositepixel.
 12. The method of claim 1, wherein the adjusting furthercomprises: adjusting the monochromatic pixels according to the gradientbetween the achromatic pixel and the achromatic pixels of neighboringachromatic pixels and a smoothing weight constant.
 13. The method ofclaim 12, wherein the instructions are further configured to select thesmoothing constant in view of a noise level of the achromatic image. 14.The method of claim 12, wherein the instructions are further configuredto adjust the monochromatic pixels according to the gradient between theachromatic pixel and the achromatic pixels of neighboring achromaticpixels and the smoothing weight constant according to a mathematicalformula comprising:${{sw}\left( {p,n} \right)} = \frac{eps}{{eps} + \left( {n - m} \right)^{2}}$wherein: p represents respective composite pixels; m represents theachromatic pixel corresponding to the composite pixel p; n representsrespective neighboring achromatic pixels of the achromatic pixel m; andeps represents the smoothing weight constant.
 15. The method of claim 1,wherein computing the composite pixels comprises de-mosaicing thecomposite pixels from the adjusted monochromatic pixels.
 16. The methodof claim 1 wherein; the achromatic comprises at least two achromaticpixels corresponding to respective monochromatic pixels; and computingthe composite pixels comprises pan-sharpening the composite pixels fromthe adjusted monochromatic pixels and the achromatic pixels.
 17. Themethod of claim 1, wherein the instructions are further configured toapply an edge preservation calculation to the composite image.
 18. Themethod of claim 17, wherein the edge preservation calculation isselected from an edge preservation calculation set comprising: ananisotropic diffusion edge preservation calculation; a bilateralfiltering edge preservation calculation; and a vectorial total variationedge preservation calculation.
 19. A camera, comprising: an achromaticimage sensor configured to capture an achromatic image of a scene; atleast two monochromatic image sensors respectively configured tocapture, concurrently with the achromatic image, a monochromatic imageof the scene; and an image generating component configured to generate acomposite image of the scene by: for respective composite pixels of thecomposite image corresponding to an achromatic pixel of the achromaticimage and at least one monochromatic pixel of respective monochromaticimages, adjusting the monochromatic pixels according to a gradientbetween the achromatic pixel and neighboring achromatic pixels toproduce a set of adjusted monochromatic pixels; and computing thecomposite pixels of the composite image from the adjusted monochromaticpixels.
 20. A nonvolatile computer-readable storage device comprisinginstructions that, when executed on a processor of a device, generate acomposite image of a scene from an achromatic image of the scene and aset of monochromatic images of the scene by: for respective compositepixels of the composite image corresponding to an achromatic pixel ofthe achromatic image and at least one monochromatic pixel of respectivemonochromatic images, adjusting the at least one monochromatic pixelsaccording to a gradient between the achromatic pixel and neighboringachromatic pixels to produce a set of adjusted monochromatic pixels,wherein the neighboring achromatic pixels comprise achromatic pixels ofthe achromatic image that are laterally and diagonally adjacent to theselected achromatic pixel; and compute the composite pixels of thecomposite image from the adjusted monochromatic pixels.